Protecting training data, model artifacts, and AI inference pipelines with purpose-built data security for the generative AI era.
Only three platforms are featured. Each is independently assessed across encryption, access architecture, threat detection, and compliance depth.
Securiti AI provides the most comprehensive data intelligence platform for organisations deploying AI at scale. Its AI-powered engine discovers sensitive data across 200+ data systems, classifies it by regulation and sensitivity, maps data flows including those feeding AI training and inference pipelines, and enforces governance policies that ensure AI systems process data in compliance with regulatory requirements. For organisations navigating EU AI Act requirements alongside GDPR, Securiti provides unified data governance that satisfies both AI governance and privacy compliance through a single platform.
BigID provides AI-powered data intelligence that helps organisations discover, classify, and manage sensitive data across their entire data estate including AI training datasets and model artifacts. Its ML-driven classification engine identifies sensitive data patterns beyond simple regex matching, understanding context and relationships within data that enable more accurate classification. For AI deployments, BigID discovers what personal and sensitive data exists in training datasets, enabling organisations to assess AI training data compliance before models enter production.
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| Capability | Securiti AI | BigID | Your Solution? |
|---|---|---|---|
| AI Training Data Discovery | ✅ AI Pipeline Monitoring | ✅ Dataset Classification | — |
| EU AI Act Compliance | ✅ AI Governance Module | ✅ Data Documentation | — |
| Sensitive Data in AI Prompts | ✅ Input/Output Monitoring | 🔶 Limited | — |
| Data Classification Depth | ✅ AI-Powered (200+ Systems) | ✅ ML-Powered (Deep Context) | — |
| Privacy Management | ✅ Full DSAR + Consent | ✅ Full Privacy Suite | — |
| Data Flow Mapping | ✅ Cross-System + AI | ✅ Data Lineage | — |
| Model Data Governance | ✅ Training Data Controls | ✅ Dataset Management | — |
| Regulatory Coverage | ✅ GDPR, EU AI Act, CCPA | ✅ GDPR, CCPA, HIPAA | — |
| Integration Breadth | ✅ 200+ Connectors | ✅ 100+ Connectors | — |
The vast majority of enterprises deploying AI have no AI-specific data security controls. Sensitive data flows into AI training pipelines, prompts, and fine-tuning datasets without the governance applied to traditional data processing.
Research shows 11% of data pasted into ChatGPT and similar AI tools contains confidential information. Without data security controls monitoring AI usage, organisations have no visibility into what sensitive data enters AI systems.
The EU AI Act requires data governance for high-risk AI systems including training data quality controls, bias assessment, and documentation of data used for training. Organisations deploying AI in the EU must demonstrate compliance with these data requirements.
Adversarial attacks against AI training data — data poisoning — introduce backdoors, biases, and vulnerabilities into AI models. Protecting training data integrity requires data security controls purpose-built for AI pipelines.
Enterprise AI adoption has outpaced data security adaptation. Organisations that spent years building data security programmes for traditional IT environments — databases, file servers, email — are now deploying AI systems that process data through entirely new pipelines: training datasets assembled from multiple sources, fine-tuning corpora containing domain-specific information, retrieval-augmented generation systems that feed enterprise data into LLM contexts, and AI agents that access and modify data autonomously.
Traditional data security tools were not designed for these workflows. They cannot monitor what data enters AI training pipelines, detect when sensitive information is included in LLM prompts, track how AI-generated content incorporates protected data, or enforce governance policies across AI processing stages. This creates the 77% gap: organisations deploying AI without extending their data security programmes to cover AI-specific data flows.
AI training data security begins with discovery: understanding what data is being used to train, fine-tune, and augment AI models. Organisations frequently discover that training datasets contain personal data subject to GDPR, customer data subject to contractual restrictions, or proprietary information that should not be used for model training. Without data security controls monitoring AI data pipelines, these exposures persist undetected.
Data security platforms like Securiti AI and BigID address this by extending data discovery and classification to AI-specific data stores — training datasets, vector databases used for RAG, fine-tuning corpora, and AI evaluation datasets. By classifying the data within these AI-specific systems, organisations can assess compliance before models enter production, remove sensitive data that should not be used for training, and document data provenance for EU AI Act requirements.
When evaluating platforms for your environment, request a proof-of-concept deployment against your actual data estate. Vendor demonstrations using sanitised demo data do not reveal how the platform performs with your specific data volumes, access complexity, and compliance requirements.
Generative AI tools create a novel data leakage vector: employees paste sensitive data into AI prompts, inadvertently exposing confidential information to third-party AI services. Research shows 11% of data entered into ChatGPT contains confidential information. This includes source code, customer data, financial projections, legal documents, and strategic plans — data that may be used to train future model versions or stored in AI service provider logs.
Addressing this requires data security controls at the AI interaction layer: monitoring what data employees paste into AI prompts, classifying prompt content for sensitivity, blocking prompts containing highly sensitive data, and logging AI interactions for compliance purposes. Securiti AI provides AI prompt monitoring capabilities that detect and control sensitive data flows to generative AI services, enabling organisations to benefit from AI productivity while preventing data leakage through AI channels.
The EU AI Act, effective 2026, introduces mandatory data governance requirements for high-risk AI systems. Article 10 requires training data to meet quality criteria including relevance, representativeness, and accuracy. Organisations must document training data sources, assess datasets for bias, and implement data governance measures throughout the AI lifecycle. For AI systems processing personal data, GDPR requirements apply simultaneously.
Data security platforms support EU AI Act compliance by providing the data intelligence capabilities the regulation demands: discovering what data is used for AI training (data inventory), classifying data for sensitivity and regulatory relevance (data classification), documenting data provenance and lineage (data mapping), and enforcing governance policies that ensure training data meets quality and compliance requirements. Organisations deploying high-risk AI systems without these data governance capabilities face regulatory action under the AI Act.
Generative AI adoption is creating new data security requirements. Ensure your platform can discover and classify sensitive data within AI training datasets, monitor data flows to AI services, and enforce policies that prevent confidential data from entering AI prompts and pipelines.
An AI data security framework addresses four data lifecycle stages. Pre-training: discover and classify data in training datasets, remove non-compliant personal data, document data sources for regulatory requirements, and assess training data for quality and bias. During training: monitor data access by training pipelines, enforce access controls on training data repositories, and log data processing activities for audit trails.
Post-deployment: monitor AI system inputs for sensitive data in prompts and contexts, classify AI outputs for sensitive data leakage, enforce policies on AI-generated content that may contain protected information, and maintain audit trails of AI data processing. Continuous governance: regular reassessment of training data compliance, monitoring for data drift in production AI systems, and automated reporting against EU AI Act and GDPR requirements. Data security platforms that cover all four stages provide comprehensive AI data governance.
AI data security maturity can be assessed across five levels. Level 1 — Unaware: no AI-specific data security controls, no visibility into AI data flows, basic acceptable use policies only. Level 2 — Aware: inventory of AI deployments exists, basic prompt monitoring deployed for high-risk AI applications, training data sources documented informally. Level 3 — Managed: automated data discovery covers AI training datasets, prompt monitoring deployed across major AI services, EU AI Act compliance assessment completed.
Level 4 — Optimised: comprehensive AI data governance across all AI lifecycle stages, automated compliance evidence for EU AI Act and GDPR, real-time monitoring of all AI data flows. Level 5 — Leading: predictive AI data risk assessment, automated remediation of AI data compliance violations, integrated AI security testing including adversarial data attacks. Most organisations are currently at Level 1 or 2 — even achieving Level 3 provides significant risk reduction and regulatory readiness.
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